tailieunhanh - Báo cáo hóa học: " Research Article An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 725189 16 pages doi 2011 725189 Research Article An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR Jonatan Lerga 1 Victor Sucic EURASIP Member 1 and Boualem Boashash2 3 1 Faculty of Engineering University of Rijeka Vukovarska 58 51000 Rijeka Croatia 2 College of Engineering Qatar University . Box 2713 Doha Qatar 3 UQ Centre for Clinical Research The University of Queensland Brisbane QLD 4072 Australia Correspondence should be addressed to Victor Sucic vsucic@ Received 14 July 2010 Revised 10 November 2010 Accepted 11 January 2011 Academic Editor Antonio Napolitano Copyright 2011 Jonatan Lerga et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A method for components instantaneous frequency IF estimation of multicomponent signals in low signal-to-noise ratio SNR is proposed. The method combines a new proposed modification of a blind source separation BSS algorithm for components separation with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals ICI rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs showing the estimation accuracy improvement in terms of the mean squared error MSE by up to 23 . Furthermore the highest improvement is achieved for low SNRs values when many of the existing methods fail. 1. Signal Model and Problem Formulation Many signals in practice such as those found in speech processing biomedical applications seismology machine condition monitoring radar sonar telecommunication and many other applications are nonstationary 1 . Those signals can be .